the SP error would tend to zero while RP would
eventually converge to a minimum, non-zero, biased
level. This, however, remains a hypothesis to be
demonstrated in the follow-up of this work.
Another interesting consequence of the nature of
SP and RP is that amplitude errors summarized in
table 3 are much lower than the corresponding time
errors (table 2). In fact as they are both associated to
a peak and an inflection, the first derivative of the
APW curve shows close-to-zero values in their
vicinity, hence the small resulting estimation error.
This fact is visible in figure 5 b) that shows the
correlation between the reference HEE and PPIA
values for RP analysis, as well the corresponding
Bland - Altman plot.
In fact, as can be seen in figure 6, AI error,
measured by the difference between AI values from
HEE and PPIA, amounts to an average value of just
0.53 %.
Figure 6: The relationships between AI obtained from
PPIA and HEE.
Table 4: Statistics information of measurements depicted
in figure 6.
5 CONCLUSIONS
We described new automatic feature extraction
algorithm capable of detecting the prominent points
of the APW: SP, DN, DP and RP. This algorithm is
a fundamental part of the automatic analysis tool in
our non-invasive system for hemodynamic analysis.
The clinical use of our probe, however, will still
require a medical oriented, multicenter study
including comparison with standard methods, e.g.
applanation tonometry and catheter collected data.
The need for a larger data base has also emerged as
the only means of attaining the necessary levels of
confidence.
ACKNOWLEDGEMENTS
We acknowledge support from Fundação para a
Ciência e a Tecnologia for funding (PTDC/SAU-
BEB/100650/2008) and SFRH/BD/61356/2009) and
from ISA, Intelligent Sensing Anywhere.
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y=0.961x+0.962
R²=0.948
‐30
‐20
‐10
0
10
20
30
40
‐30 ‐20 ‐10 0 10203040
AI_PPIA (%)
AI_HEE (%)
N
Minimum
(%)
Maximum
(%)
Mean
(%)
Std.
Deviation (%)
Error 167 0.00 19.09 0.53 2.47
Descriptive Statistics
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